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        <h1 id="Python参考书籍"><a href="#Python参考书籍" class="headerlink" title="Python参考书籍"></a>Python参考书籍</h1><h2 id="入门读物"><a href="#入门读物" class="headerlink" title="入门读物"></a>入门读物</h2><ol>
<li>《Python基础教程》（Beginning Python From Novice to Professional）</li>
<li>《Python学习手册》（Learning Python）</li>
<li>《Python编程》（Programming Python）</li>
<li>《Python Cookbook》</li>
<li>《Python程序设计》（Python Programming: An Introduction to Computer Science）</li>
<li>《Modern Python Cookbook》</li>
</ol>
<h2 id="进阶读物"><a href="#进阶读物" class="headerlink" title="进阶读物"></a>进阶读物</h2><ol>
<li>《Python核心编程》（Core Python Applications Programming）</li>
<li>《流畅的Python》（Fluent Python）</li>
<li>《Effective Python：编写高质量Python代码的59个有效方法》（Effective Python 59 Specific Ways to Write Better Python）</li>
<li>《Python设计模式》（Learning Python Design Patterns）</li>
<li>《Python高级编程》（Expert Python Programming）</li>
<li>《Python性能分析与优化》（Mastering Python High Performance）</li>
</ol>
<a id="more"></a>

<h2 id="Web框架"><a href="#Web框架" class="headerlink" title="Web框架"></a>Web框架</h2><ol>
<li>《Django基础教程》（Tango with Django）</li>
<li>《轻量级Django》（Lightweight Django）</li>
<li>《Python Web开发：测试驱动方法》（Test-Driven Development with Python）</li>
<li>《Web Development with Django Cookbook》</li>
<li>《Test-Driven Development with Django》</li>
<li>《Django Project Blueprints 》</li>
<li>《Flask Web开发：基于Python的Web应用开发实战》（Flask Web Development: Developing Web Applications with Python）</li>
<li>《深入理解Flask》（Mastering Flask）</li>
</ol>
<h2 id="爬虫开发"><a href="#爬虫开发" class="headerlink" title="爬虫开发"></a>爬虫开发</h2><ol>
<li>《用Python写网络爬虫》（Web Scraping with Python）</li>
<li>《精通Python爬虫框架Scrapy》（Learning Scrapy）</li>
<li>《Python网络数据采集》（Web Scraping with Python）</li>
<li>《Python爬虫开发与项目实战》</li>
<li>《Python 3网络爬虫开发实战》</li>
</ol>
<h2 id="数据分析"><a href="#数据分析" class="headerlink" title="数据分析"></a>数据分析</h2><ol>
<li>《利用Python进行数据分析》（Python for Data Analysis）</li>
<li>《Python数据科学手册》（Python Data Science Handbook）</li>
<li>《Python金融大数据分析》（Python for Finance）</li>
<li>《Python数据可视化编程实战》（Python Data Visualization Cookbook）</li>
<li>《Python数据处理》（Data Wrangling with Python）</li>
</ol>
<h2 id="机器学习"><a href="#机器学习" class="headerlink" title="机器学习"></a>机器学习</h2><ol>
<li>《Python机器学习基础教程》（Introduction to Machine Learning with Python）</li>
<li>《Python机器学习实践指南》（Python Machine Learning Blueprints）</li>
<li>《Python Machine Learning Case Studies》</li>
<li>《Python机器学习实践：测试驱动的开发方法》（Thoughtful Machine Learning with Python A Test Driven Approach）</li>
<li>《Python机器学习经典实例》（Python Machine Learning Cookbook）</li>
<li>《TensorFlow：实战Google深度学习框架》</li>
</ol>
<hr>
<h2 id="知乎问题回答"><a href="#知乎问题回答" class="headerlink" title="知乎问题回答"></a>知乎问题回答</h2><h3 id="Python学习完基础语法知识后，如何进一步提高？"><a href="#Python学习完基础语法知识后，如何进一步提高？" class="headerlink" title="Python学习完基础语法知识后，如何进一步提高？"></a>Python学习完基础语法知识后，如何进一步提高？</h3><p>如果你已经完成了Python基础语法的学习，想要知道接下来如何提高，那么你得先问问自己你要用Python来做什么？目前学习Python后可能的就业方向包括以下几个领域，我把每个领域需要的技术作为了一个简单的关键词摘要。</p>
<blockquote>
<p>说明：以下数据参考了主要的招聘门户网站以及职友集。</p>
</blockquote>
<table>
<thead>
<tr>
<th>职位</th>
<th>所需技能</th>
<th>招聘需求量</th>
</tr>
</thead>
<tbody><tr>
<td>Python后端开发工程师</td>
<td>Python基础<br>Django / Flask / Tornado / Sanic<br>RESTful / 接口文档撰写<br>MySQL / Redis / MongoDB / ElasticSearch<br>Linux / Git / Scrum / PyCharm</td>
<td>大</td>
</tr>
<tr>
<td>Python爬虫开发工程师</td>
<td>Python基础<br>常用标准库和三方库<br>Scrapy / PySpider<br>Selenium / Appnium<br>Redis / MongoDB / MySQL<br>前端 / HTTP(S) / 抓包工具</td>
<td>较少</td>
</tr>
<tr>
<td>Python量化交易开发工程师</td>
<td>Python基础<br>数据结构 / 算法 / 设计模式<br>NoSQL（KV数据库）<br>金融学（两融、期权、期货、股票） / 数字货币</td>
<td>较大（一线城市）</td>
</tr>
<tr>
<td>Python数据分析工程师 /<br>Python机器学习工程师</td>
<td>统计学专业 / 数学专业 / 计算机专业<br>Python基础 / 算法设计<br>SQL / NoSQL / Hive / Hadoop / Spark<br>NumPy / Scikit-Learn / Pandas / Seaborn<br>PyTorch / Tensorflow / OpenCV</td>
<td>较大（一线城市）</td>
</tr>
<tr>
<td>Python自动化测试工程师</td>
<td>Python基础 / 单元测试 / 软件测试基础<br>Linux / Shell / JIRA / 禅道 / Jenkins / CI / CD<br>Selenium / Robot Framework / Appnium<br>ab / sysbench / JMeter / LoadRunner / QTP</td>
<td>大</td>
</tr>
<tr>
<td>Python自动化运维工程师</td>
<td>Python基础 / Linux / Shell <br>Fabric / Ansible / Playbook<br>Zabbix / Saltstack / Puppet<br>Docker / paramiko</td>
<td>较大（一线城市）</td>
</tr>
<tr>
<td>Python云平台开发工程师</td>
<td>Python基础<br>OpenStack / CloudStack<br>Ovirt / KVM<br>Docker / K8S</td>
<td>较少（一线城市）</td>
</tr>
</tbody></table>
<p>如果弄清了自己将来要做的方向，就可以开始有针对性的学习了，下面给大家一个推荐书籍的清单。</p>
<ol>
<li><p>入门读物</p>
<ul>
<li>《Python基础教程》（<em>Beginning Python From Novice to Professional</em>）</li>
<li>《Python学习手册》（<em>Learning Python</em>）</li>
<li>《Python编程》（<em>Programming Python</em>）</li>
<li>《Python编程从入门到实践》（<em>Python Crash Course</em>）</li>
<li>《Python Cookbook》</li>
</ul>
</li>
<li><p>进阶读物</p>
<ul>
<li>《软件架构 - Python语言实现》（<em>Software Architecture with Python</em>）</li>
<li>《流畅的Python》（<em>Fluent Python</em>）</li>
<li>《Python设计模式》（<em>Learning Python Design Patterns</em>）</li>
<li>《Python高级编程》（<em>Expert Python Programming</em>）</li>
<li>《Python性能分析与优化》（<em>Mastering Python High Performance</em>）</li>
</ul>
</li>
<li><p>数据库相关</p>
<ul>
<li>《MySQL必知必会》（<em>MySQL Crash Course</em>）</li>
<li>《深入浅出MySQL - 数据库开发、优化与管理维护》</li>
<li>《MongoDB权威指南》（<em>MongoDB: The Definitive Guide</em>）</li>
<li>《Redis实战》（<em>Redis in Action</em>）</li>
<li>《Redis开发与运维》</li>
</ul>
</li>
<li><p>Linux / Shell / Docker / 运维</p>
<ul>
<li>《鸟哥的Linux私房菜》</li>
<li>《Linux命令行与shell脚本编程大全》（<em>Linux Command Line and Shell Scripting Bible</em>）</li>
<li>《Python自动化运维:技术与最佳实践》</li>
<li>《第一本Docker书》（<em>The Docker Book</em>）</li>
<li>《Docker经典实例》（Docker Cookbook）</li>
</ul>
</li>
<li><p>Django / Flask / Tornado</p>
<ul>
<li><p>《Django基础教程》（<em>Tango with Django</em>）</p>
</li>
<li><p>《轻量级Django》（<em>Lightweight Django</em>）</p>
</li>
<li><p>《精通Django》（<em>Mastering Django: Core</em>）</p>
</li>
<li><p>《Python Web开发：测试驱动方法》（<em>Test-Driven Development with Python</em>）</p>
</li>
<li><p>《Two Scoops of Django: Best Practice of Django 1.8》</p>
</li>
<li><p>《Flask Web开发：基于Python的Web应用开发实战》（<em>Flask Web Development: Developing Web Applications with Python</em>）</p>
</li>
<li><p>《深入理解Flask》（<em>Mastering Flask</em>）</p>
</li>
<li><p>《Introduction to Tornado》</p>
</li>
</ul>
</li>
<li><p>爬虫开发</p>
<ul>
<li><p>《用Python写网络爬虫》（<em>Web Scraping with Python</em>）</p>
</li>
<li><p>《精通Python爬虫框架Scrapy》（<em>Learning Scrapy</em>）</p>
</li>
<li><p>《Python网络数据采集》（<em>Web Scraping with Python</em>）</p>
</li>
<li><p>《Python爬虫开发与项目实战》</p>
</li>
<li><p>《Python 3网络爬虫开发实战》</p>
</li>
</ul>
</li>
<li><p>数据分析</p>
<ul>
<li>《利用Python进行数据分析》（<em>Python for Data Analysis</em>）</li>
<li>《Python数据科学手册》（<em>Python Data Science Handbook</em>）</li>
<li>《Python金融大数据分析》（<em>Python for Finance</em>）</li>
<li>《Python数据可视化编程实战》（<em>Python Data Visualization Cookbook</em>）</li>
<li>《Python数据处理》（<em>Data Wrangling with Python</em>）</li>
</ul>
</li>
<li><p>机器学习</p>
<ul>
<li><p>《Python机器学习基础教程》（<em>Introduction to Machine Learning with Python</em>）</p>
</li>
<li><p>《Python机器学习实践指南》（<em>Python Machine Learning Blueprints</em>）</p>
</li>
<li><p>《Python机器学习实践：测试驱动的开发方法》（<em>Thoughtful Machine Learning with Python A Test Driven Approach</em>）</p>
</li>
<li><p>《Python机器学习经典实例》（<em>Python Machine Learning Cookbook</em>）</p>
</li>
<li><p>《TensorFlow：实战Google深度学习框架》</p>
</li>
</ul>
</li>
<li><p>其他书籍</p>
<ul>
<li>《Pro Git》</li>
<li>《Selenium自动化测试 - 基于Python语言》（<em>Learning Selenium Testing Tools with Python</em>）</li>
<li>《Selenium自动化测试之道》</li>
<li>《Scrum敏捷软件开发》（<em>Software Development using Scrum</em>）</li>
<li>《高效团队开发 - 工具与方法》</li>
</ul>
</li>
</ol>
<p>当然学习编程，最重要的通过项目实战来提升自己的综合能力，Github上有大量的优质开源项目，其中不乏优质的Python项目。有一个名为<a href="https://github.com/mahmoud/awesome-python-applications" target="_blank" rel="noopener">“awesome-python-applications”</a>的项目对这些优质的资源进行了归类并提供了传送门，大家可以了解下。除此之外，还要为大家推荐一个名为<a href="https://github.com/jackfrued/Python-100-Days" target="_blank" rel="noopener">“Python-100-Days”</a>的项目，上面有大量优质的Python学习资料（包括文档、代码和相关资源）。如果自学能力不是那么强，可以通过网络上免费或者付费的视频课程来学习对应的知识；如果自律性没有那么强，那就只能建议花钱参加培训班了，因为花钱在有人监督的环境下学习对很多人来说确实是一个捷径，但是要记得：“师傅领进门，修行靠各人”。选择自己热爱的东西并全力以赴，不要盲目的跟风学习，这一点算是过来人的忠告吧。记得我自己刚开始进入软件开发这个行业时，有人跟我说过这么一句话，现在也分享出来与诸君共勉：“浮躁的人有两种：只观望而不学习的人，只学习而不坚持的人；浮躁的人都不是高手。”</p>

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